variant-pathogenicity-predictor

Integrate REVEL, CADD, PolyPhen scores to predict variant pathogenicity

Safety Notice

This listing is from the official public ClawHub registry. Review SKILL.md and referenced scripts before running.

Copy this and send it to your AI assistant to learn

Install skill "variant-pathogenicity-predictor" with this command: npx skills add EC-cyber258/variant-pathogenicity-predictor

Variant Pathogenicity Predictor

Integrate REVEL, CADD, PolyPhen and other scores to predict variant pathogenicity.

Usage

python scripts/main.py --variant "chr17:43094692:G:A" --gene "BRCA1"
python scripts/main.py --vcf variants.vcf --output report.json

Parameters

  • --variant: Variant in format chr:pos:ref:alt
  • --vcf: VCF file with variants
  • --gene: Gene symbol
  • --scores: Prediction scores to use (REVEL,CADD,PolyPhen)

Integrated Scores

  • REVEL (Rare Exome Variant Ensemble Learner)
  • CADD (Combined Annotation Dependent Depletion)
  • PolyPhen-2 (Polymorphism Phenotyping)
  • SIFT (Sorting Intolerant From Tolerant)
  • MutationTaster

Output

  • Pathogenicity classification
  • ACMG guideline interpretation
  • Individual score breakdown
  • Confidence assessment

Risk Assessment

Risk IndicatorAssessmentLevel
Code ExecutionPython/R scripts executed locallyMedium
Network AccessNo external API callsLow
File System AccessRead input files, write output filesMedium
Instruction TamperingStandard prompt guidelinesLow
Data ExposureOutput files saved to workspaceLow

Security Checklist

  • No hardcoded credentials or API keys
  • No unauthorized file system access (../)
  • Output does not expose sensitive information
  • Prompt injection protections in place
  • Input file paths validated (no ../ traversal)
  • Output directory restricted to workspace
  • Script execution in sandboxed environment
  • Error messages sanitized (no stack traces exposed)
  • Dependencies audited

Prerequisites

No additional Python packages required.

Evaluation Criteria

Success Metrics

  • Successfully executes main functionality
  • Output meets quality standards
  • Handles edge cases gracefully
  • Performance is acceptable

Test Cases

  1. Basic Functionality: Standard input → Expected output
  2. Edge Case: Invalid input → Graceful error handling
  3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

  • Current Stage: Draft
  • Next Review Date: 2026-03-06
  • Known Issues: None
  • Planned Improvements:
    • Performance optimization
    • Additional feature support

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.